{ "cells": [ { "cell_type": "code", "execution_count": 459, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "pd.set_option('display.max_columns', 50000)\n", "import numpy as np\n", "import csv\n", "import json\n", "import matplotlib.pyplot as plt\n", "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.preprocessing import OneHotEncoder\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.metrics.pairwise import cosine_similarity" ] }, { "cell_type": "code", "execution_count": 460, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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user_idusernameagestatussexorientationdrinksdrugsheightjoblocationpetssmokeslanguagenew_languagesbody_profileeducation_leveldropped_outbiointerestsother_interestslocation_preference
0fffe3100Edith Lopez27singlefgaysociallynever66.0medicine / healthoakland, californialikes dogs and likes catsnoenglish (fluently), spanish (poorly), sign lan...interestedathletic4.0nobottom line i love life! i work hard and i lov...sportsinstrumentssame state
1fffe3200Travis Young26singlemgaysociallynever68.0otherpleasant hill, californialikes dogsnoenglish (fluently), tagalog (okay), french (po...interestedfit3.0noi'm a straightforward, genuine, fun loving (i'...paintinginstrumentsanywhere
2fffe3300Agnes Smith20seeing someonefbisexualsociallysometimes69.0otheroakland, californiahas dogs and likes catssometimesenglish (fluently), sign language (poorly), fr...interestedfit2.0nommmmm yummy tacosss. yoga is where it's at. i ...instrumentsdancingsame city
3fffe3400Salvador Klaver27singlembisexualsociallysometimes68.0computer / hardware / softwaredaly city, californialikes dogs and likes catsnoenglishnot interestedaverage3.0noi'm a stealth geek. that special mix of techni...sketchingactingsame city
4fffe3500Elana Sewell22singlefbisexualoftensometimes68.0otheroakland, californialikes dogs and likes catsyesenglishnot interestedaverage2.0yeswith the whisper of the wind i was weaved into...craftdesigningsame city
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" ], "text/plain": [ " user_id username age status sex orientation drinks \\\n", "0 fffe3100 Edith Lopez 27 single f gay socially \n", "1 fffe3200 Travis Young 26 single m gay socially \n", "2 fffe3300 Agnes Smith 20 seeing someone f bisexual socially \n", "3 fffe3400 Salvador Klaver 27 single m bisexual socially \n", "4 fffe3500 Elana Sewell 22 single f bisexual often \n", "\n", " drugs height job \\\n", "0 never 66.0 medicine / health \n", "1 never 68.0 other \n", "2 sometimes 69.0 other \n", "3 sometimes 68.0 computer / hardware / software \n", "4 sometimes 68.0 other \n", "\n", " location pets smokes \\\n", "0 oakland, california likes dogs and likes cats no \n", "1 pleasant hill, california likes dogs no \n", "2 oakland, california has dogs and likes cats sometimes \n", "3 daly city, california likes dogs and likes cats no \n", "4 oakland, california likes dogs and likes cats yes \n", "\n", " language new_languages \\\n", "0 english (fluently), spanish (poorly), sign lan... interested \n", "1 english (fluently), tagalog (okay), french (po... interested \n", "2 english (fluently), sign language (poorly), fr... interested \n", "3 english not interested \n", "4 english not interested \n", "\n", " body_profile education_level dropped_out \\\n", "0 athletic 4.0 no \n", "1 fit 3.0 no \n", "2 fit 2.0 no \n", "3 average 3.0 no \n", "4 average 2.0 yes \n", "\n", " bio interests \\\n", "0 bottom line i love life! i work hard and i lov... sports \n", "1 i'm a straightforward, genuine, fun loving (i'... painting \n", "2 mmmmm yummy tacosss. yoga is where it's at. i ... instruments \n", "3 i'm a stealth geek. that special mix of techni... sketching \n", "4 with the whisper of the wind i was weaved into... craft \n", "\n", " other_interests location_preference \n", "0 instruments same state \n", "1 instruments anywhere \n", "2 dancing same city \n", "3 acting same city \n", "4 designing same city " ] }, "execution_count": 460, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(filepath_or_buffer=\"./dataset/data.csv\")\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 461, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ageheighteducation_level
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75%39.00000071.0000003.000000
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" ], "text/plain": [ " age height education_level\n", "count 2001.000000 2001.000000 2001.000000\n", "mean 33.072464 68.289855 2.932534\n", "std 10.483189 3.895246 0.812371\n", "min 18.000000 55.000000 1.000000\n", "25% 26.000000 65.000000 2.000000\n", "50% 30.000000 68.000000 3.000000\n", "75% 39.000000 71.000000 3.000000\n", "max 69.000000 83.000000 5.000000" ] }, "execution_count": 461, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 462, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 2001 entries, 0 to 2000\n", "Data columns (total 22 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 user_id 2001 non-null object \n", " 1 username 2001 non-null object \n", " 2 age 2001 non-null int64 \n", " 3 status 2001 non-null object \n", " 4 sex 2001 non-null object \n", " 5 orientation 2001 non-null object \n", " 6 drinks 2001 non-null object \n", " 7 drugs 2001 non-null object \n", " 8 height 2001 non-null float64\n", " 9 job 2001 non-null object \n", " 10 location 2001 non-null object \n", " 11 pets 2001 non-null object \n", " 12 smokes 2001 non-null object \n", " 13 language 2001 non-null object \n", " 14 new_languages 2001 non-null object \n", " 15 body_profile 2001 non-null object \n", " 16 education_level 2001 non-null float64\n", " 17 dropped_out 2001 non-null object \n", " 18 bio 2001 non-null object \n", " 19 interests 2001 non-null object \n", " 20 other_interests 2001 non-null object \n", " 21 location_preference 2001 non-null object \n", "dtypes: float64(2), int64(1), object(19)\n", "memory usage: 344.0+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 463, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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user_idusernameagestatussexorientationdrinksdrugsheightjoblocationpetssmokeslanguagenew_languagesbody_profileeducation_leveldropped_outbiointerestsother_interestslocation_preferencelikes_dogslikes_catshas_catshas_dogslocation_statelocation_citylanguage_json
0fffe3100Edith Lopez27singlefgaysociallynever66.0medicine / healthoakland, californialikes dogs and likes catsnoenglish (fluently), spanish (poorly), sign lan...interestedathletic4.0nobottom line i love life! i work hard and i lov...sportsinstrumentssame stateFalseFalseFalseFalseNoneNoneNone
1fffe3200Travis Young26singlemgaysociallynever68.0otherpleasant hill, californialikes dogsnoenglish (fluently), tagalog (okay), french (po...interestedfit3.0noi'm a straightforward, genuine, fun loving (i'...paintinginstrumentsanywhereFalseFalseFalseFalseNoneNoneNone
2fffe3300Agnes Smith20seeing someonefbisexualsociallysometimes69.0otheroakland, californiahas dogs and likes catssometimesenglish (fluently), sign language (poorly), fr...interestedfit2.0nommmmm yummy tacosss. yoga is where it's at. i ...instrumentsdancingsame cityFalseFalseFalseFalseNoneNoneNone
3fffe3400Salvador Klaver27singlembisexualsociallysometimes68.0computer / hardware / softwaredaly city, californialikes dogs and likes catsnoenglishnot interestedaverage3.0noi'm a stealth geek. that special mix of techni...sketchingactingsame cityFalseFalseFalseFalseNoneNoneNone
4fffe3500Elana Sewell22singlefbisexualoftensometimes68.0otheroakland, californialikes dogs and likes catsyesenglishnot interestedaverage2.0yeswith the whisper of the wind i was weaved into...craftdesigningsame cityFalseFalseFalseFalseNoneNoneNone
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" ], "text/plain": [ " user_id username age status sex orientation drinks \\\n", "0 fffe3100 Edith Lopez 27 single f gay socially \n", "1 fffe3200 Travis Young 26 single m gay socially \n", "2 fffe3300 Agnes Smith 20 seeing someone f bisexual socially \n", "3 fffe3400 Salvador Klaver 27 single m bisexual socially \n", "4 fffe3500 Elana Sewell 22 single f bisexual often \n", "\n", " drugs height job \\\n", "0 never 66.0 medicine / health \n", "1 never 68.0 other \n", "2 sometimes 69.0 other \n", "3 sometimes 68.0 computer / hardware / software \n", "4 sometimes 68.0 other \n", "\n", " location pets smokes \\\n", "0 oakland, california likes dogs and likes cats no \n", "1 pleasant hill, california likes dogs no \n", "2 oakland, california has dogs and likes cats sometimes \n", "3 daly city, california likes dogs and likes cats no \n", "4 oakland, california likes dogs and likes cats yes \n", "\n", " language new_languages \\\n", "0 english (fluently), spanish (poorly), sign lan... interested \n", "1 english (fluently), tagalog (okay), french (po... interested \n", "2 english (fluently), sign language (poorly), fr... interested \n", "3 english not interested \n", "4 english not interested \n", "\n", " body_profile education_level dropped_out \\\n", "0 athletic 4.0 no \n", "1 fit 3.0 no \n", "2 fit 2.0 no \n", "3 average 3.0 no \n", "4 average 2.0 yes \n", "\n", " bio interests \\\n", "0 bottom line i love life! i work hard and i lov... sports \n", "1 i'm a straightforward, genuine, fun loving (i'... painting \n", "2 mmmmm yummy tacosss. yoga is where it's at. i ... instruments \n", "3 i'm a stealth geek. that special mix of techni... sketching \n", "4 with the whisper of the wind i was weaved into... craft \n", "\n", " other_interests location_preference likes_dogs likes_cats has_cats \\\n", "0 instruments same state False False False \n", "1 instruments anywhere False False False \n", "2 dancing same city False False False \n", "3 acting same city False False False \n", "4 designing same city False False False \n", "\n", " has_dogs location_state location_city language_json \n", "0 False None None None \n", "1 False None None None \n", "2 False None None None \n", "3 False None None None \n", "4 False None None None " ] }, "execution_count": 463, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"likes_dogs\"] = False\n", "df[\"likes_cats\"] = False\n", "df[\"has_cats\"] = False\n", "df[\"has_dogs\"] = False\n", "df[\"location_state\"] = None\n", "df[\"location_city\"] = None\n", "df[\"language_json\"] = None\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 464, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "single 1867\n", "available 66\n", "seeing someone 54\n", "married 14\n", "Name: status, dtype: int64\n", "--------------------------------------------------\n", "m 1139\n", "f 862\n", "Name: sex, dtype: int64\n", "--------------------------------------------------\n", "straight 1736\n", "gay 153\n", "bisexual 112\n", "Name: orientation, dtype: int64\n", "--------------------------------------------------\n", "socially 1393\n", "rarely 256\n", "often 168\n", "not at all 157\n", "very often 16\n", "desperately 11\n", "Name: drinks, dtype: int64\n", "--------------------------------------------------\n", "never 1586\n", "sometimes 397\n", "often 18\n", "Name: drugs, dtype: int64\n", "--------------------------------------------------\n", "no 1630\n", "sometimes 128\n", "yes 94\n", "when drinking 91\n", "trying to quit 58\n", "Name: smokes, dtype: int64\n", "--------------------------------------------------\n", "not interested 1050\n", "interested 633\n", "somewhat interested 318\n", "Name: new_languages, dtype: int64\n", "--------------------------------------------------\n", "average 557\n", "fit 433\n", "athletic 385\n", "curvy 179\n", "thin 161\n", "a little extra 110\n", "skinny 65\n", "full figured 43\n", "overweight 25\n", "used up 20\n", "jacked 13\n", "rather not say 10\n", "Name: body_profile, dtype: int64\n", "--------------------------------------------------\n", "no 1898\n", "yes 103\n", "Name: dropped_out, dtype: int64\n", "--------------------------------------------------\n", "music 155\n", "politics 105\n", "reading 105\n", "singing 101\n", "photography 92\n", "instruments 88\n", "video games 87\n", "sports 83\n", "painting 83\n", "movies 79\n", "dancing 75\n", "travelling 74\n", "cooking 67\n", "designing 64\n", "craft 64\n", "sketching 64\n", "food 60\n", "diy 49\n", "gardening 48\n", "studying 47\n", "writting 47\n", "yoga 46\n", "social_networking 46\n", "organising events 43\n", "makeup 42\n", "acting 41\n", "astronomy 37\n", "calligraphy 33\n", "fishing 33\n", "collectibles 27\n", "camping 16\n", "Name: interests, dtype: int64\n", "--------------------------------------------------\n", "music 158\n", "sports 102\n", "photography 100\n", "reading 100\n", "singing 98\n", "politics 94\n", "video games 90\n", "dancing 88\n", "painting 85\n", "movies 81\n", "craft 78\n", "instruments 72\n", "sketching 67\n", "travelling 62\n", "designing 61\n", "makeup 57\n", "social_networking 57\n", "food 54\n", "diy 53\n", "writting 50\n", "cooking 49\n", "gardening 49\n", "acting 45\n", "fishing 38\n", "studying 36\n", "astronomy 35\n", "yoga 35\n", "organising events 34\n", "calligraphy 33\n", "collectibles 21\n", "camping 19\n", "Name: other_interests, dtype: int64\n", "--------------------------------------------------\n", "same city 1169\n", "same state 495\n", "anywhere 337\n", "Name: location_preference, dtype: int64\n", "--------------------------------------------------\n", "san francisco, california 911\n", "oakland, california 250\n", "berkeley, california 146\n", "san mateo, california 56\n", "palo alto, california 45\n", " ... \n", "nha trang, vietnam 1\n", "canyon country, california 1\n", "brisbane, california 1\n", "phoenix, arizona 1\n", "foster city, california 1\n", "Name: location, Length: 70, dtype: int64\n", "--------------------------------------------------\n", "likes dogs and likes cats 743\n", "likes dogs 303\n", "likes dogs and has cats 229\n", "has dogs 184\n", "has dogs and likes cats 148\n", "likes dogs and dislikes cats 121\n", "has dogs and has cats 87\n", "has cats 63\n", "likes cats 59\n", "has dogs and dislikes cats 24\n", "dislikes dogs and dislikes cats 17\n", "dislikes dogs and likes cats 9\n", "dislikes dogs 5\n", "dislikes cats 5\n", "dislikes dogs and has cats 4\n", "Name: pets, dtype: int64\n", "--------------------------------------------------\n", "--------------------------------------------------\n", "--------------------------------------------------\n", "False 2001\n", "Name: likes_dogs, dtype: int64\n", "--------------------------------------------------\n", "False 2001\n", "Name: likes_cats, dtype: int64\n", "--------------------------------------------------\n", "False 2001\n", "Name: has_cats, dtype: int64\n", "--------------------------------------------------\n", "False 2001\n", "Name: has_dogs, dtype: int64\n", "--------------------------------------------------\n", "Series([], Name: location_state, dtype: int64)\n", "--------------------------------------------------\n", "Series([], Name: location_city, dtype: int64)\n" ] } ], "source": [ "print(df[\"status\"].value_counts())\n", "print(\"-\"*50)\n", "print(df[\"sex\"].value_counts())\n", "print(\"-\"*50)\n", "print(df[\"orientation\"].value_counts())\n", "print(\"-\"*50)\n", "print(df[\"drinks\"].value_counts())\n", "print(\"-\"*50)\n", "print(df[\"drugs\"].value_counts())\n", "print(\"-\"*50)\n", "print(df[\"smokes\"].value_counts())\n", "print(\"-\"*50)\n", "print(df[\"new_languages\"].value_counts())\n", "print(\"-\"*50)\n", "print(df[\"body_profile\"].value_counts())\n", "print(\"-\"*50)\n", "print(df[\"dropped_out\"].value_counts())\n", "print(\"-\"*50)\n", "print(df[\"interests\"].value_counts())\n", "print(\"-\"*50)\n", "print(df[\"other_interests\"].value_counts())\n", "print(\"-\"*50)\n", "print(df[\"location_preference\"].value_counts())\n", "print(\"-\"*50)\n", "print(df[\"location\"].value_counts())\n", "print(\"-\"*50)\n", "print(df[\"pets\"].value_counts())\n", "print(\"-\"*50)\n", "print(\"-\"*50)\n", "print(\"-\"*50)\n", "print(df[\"likes_dogs\"].value_counts())\n", "print(\"-\"*50)\n", "print(df[\"likes_cats\"].value_counts())\n", "print(\"-\"*50)\n", "print(df[\"has_cats\"].value_counts())\n", "print(\"-\"*50)\n", "print(df[\"has_dogs\"].value_counts())\n", "print(\"-\"*50)\n", "print(df[\"location_state\"].value_counts())\n", "print(\"-\"*50)\n", "print(df[\"location_city\"].value_counts())" ] }, { "cell_type": "code", "execution_count": 465, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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user_idusernameagestatussexorientationdrinksdrugsheightjoblocationpetssmokeslanguagenew_languagesbody_profileeducation_leveldropped_outbiointerestsother_interestslocation_preferencelikes_dogslikes_catshas_catshas_dogslocation_statelocation_citylanguage_json
0fffe3100Edith Lopez27singlefgaysociallynever66.0medicine/healthoakland, californialikes dogs and likes catsnoenglish (fluently), spanish (poorly), sign lan...interestedathletic4.0nobottom line i love life! i work hard and i lov...sportsinstrumentssame stateTrueTrueFalseFalsecaliforniaoakland[{\"language\": \"english\", \"proficiency\": \"fluen...
1fffe3200Travis Young26singlemgaysociallynever68.0otherpleasant hill, californialikes dogsnoenglish (fluently), tagalog (okay), french (po...interestedfit3.0noi'm a straightforward, genuine, fun loving (i'...paintinginstrumentsanywhereTrueFalseFalseFalsecaliforniapleasant hill[{\"language\": \"english\", \"proficiency\": \"fluen...
2fffe3300Agnes Smith20seeing someonefbisexualsociallysometimes69.0otheroakland, californiahas dogs and likes catssometimesenglish (fluently), sign language (poorly), fr...interestedfit2.0nommmmm yummy tacosss. yoga is where it's at. i ...instrumentsdancingsame cityFalseTrueFalseTruecaliforniaoakland[{\"language\": \"english\", \"proficiency\": \"fluen...
3fffe3400Salvador Klaver27singlembisexualsociallysometimes68.0computer/hardware/softwaredaly city, californialikes dogs and likes catsnoenglishnot interestedaverage3.0noi'm a stealth geek. that special mix of techni...sketchingactingsame cityTrueTrueFalseFalsecaliforniadaly city[{\"language\": \"english\", \"proficiency\": \"fluen...
4fffe3500Elana Sewell22singlefbisexualoftensometimes68.0otheroakland, californialikes dogs and likes catsyesenglishnot interestedaverage2.0yeswith the whisper of the wind i was weaved into...craftdesigningsame cityTrueTrueFalseFalsecaliforniaoakland[{\"language\": \"english\", \"proficiency\": \"fluen...
\n", "
" ], "text/plain": [ " user_id username age status sex orientation drinks \\\n", "0 fffe3100 Edith Lopez 27 single f gay socially \n", "1 fffe3200 Travis Young 26 single m gay socially \n", "2 fffe3300 Agnes Smith 20 seeing someone f bisexual socially \n", "3 fffe3400 Salvador Klaver 27 single m bisexual socially \n", "4 fffe3500 Elana Sewell 22 single f bisexual often \n", "\n", " drugs height job location \\\n", "0 never 66.0 medicine/health oakland, california \n", "1 never 68.0 other pleasant hill, california \n", "2 sometimes 69.0 other oakland, california \n", "3 sometimes 68.0 computer/hardware/software daly city, california \n", "4 sometimes 68.0 other oakland, california \n", "\n", " pets smokes \\\n", "0 likes dogs and likes cats no \n", "1 likes dogs no \n", "2 has dogs and likes cats sometimes \n", "3 likes dogs and likes cats no \n", "4 likes dogs and likes cats yes \n", "\n", " language new_languages \\\n", "0 english (fluently), spanish (poorly), sign lan... interested \n", "1 english (fluently), tagalog (okay), french (po... interested \n", "2 english (fluently), sign language (poorly), fr... interested \n", "3 english not interested \n", "4 english not interested \n", "\n", " body_profile education_level dropped_out \\\n", "0 athletic 4.0 no \n", "1 fit 3.0 no \n", "2 fit 2.0 no \n", "3 average 3.0 no \n", "4 average 2.0 yes \n", "\n", " bio interests \\\n", "0 bottom line i love life! i work hard and i lov... sports \n", "1 i'm a straightforward, genuine, fun loving (i'... painting \n", "2 mmmmm yummy tacosss. yoga is where it's at. i ... instruments \n", "3 i'm a stealth geek. that special mix of techni... sketching \n", "4 with the whisper of the wind i was weaved into... craft \n", "\n", " other_interests location_preference likes_dogs likes_cats has_cats \\\n", "0 instruments same state True True False \n", "1 instruments anywhere True False False \n", "2 dancing same city False True False \n", "3 acting same city True True False \n", "4 designing same city True True False \n", "\n", " has_dogs location_state location_city \\\n", "0 False california oakland \n", "1 False california pleasant hill \n", "2 True california oakland \n", "3 False california daly city \n", "4 False california oakland \n", "\n", " language_json \n", "0 [{\"language\": \"english\", \"proficiency\": \"fluen... \n", "1 [{\"language\": \"english\", \"proficiency\": \"fluen... \n", "2 [{\"language\": \"english\", \"proficiency\": \"fluen... \n", "3 [{\"language\": \"english\", \"proficiency\": \"fluen... \n", "4 [{\"language\": \"english\", \"proficiency\": \"fluen... " ] }, "execution_count": 465, "metadata": {}, "output_type": "execute_result" } ], "source": [ "total_languages = []\n", "total_language_proficiency = []\n", "for ind in df.index:\n", " \n", " pets = df[\"pets\"][ind]\n", " \n", " if \"likes dogs\" in pets:\n", " df.loc[ind,\"likes_dogs\"] = True\n", " elif \"dislikes dogs\" in pets:\n", " df.loc[ind,\"likes_dogs\"] = False\n", " else:\n", " df.loc[ind,\"likes_dogs\"] = False\n", "\n", " if \"likes cats\" in pets:\n", " df.loc[ind,\"likes_cats\"] = True\n", " elif \"dislikes cats\" in pets:\n", " df.loc[ind,\"likes_cats\"] = False\n", " else:\n", " df.loc[ind,\"likes_cats\"] = False\n", "\n", " if \"has dogs\" in pets:\n", " df.loc[ind,\"has_dogs\"] = True\n", " else:\n", " df.loc[ind,\"has_dogs\"] = False\n", "\n", " if \"has cats\" in pets:\n", " df.loc[ind,\"has_cats\"] = True\n", " else:\n", " df.loc[ind,\"has_cats\"] = False\n", "\n", " \n", " location = df[\"location\"][ind]\n", " x = location.split(\",\")\n", " x[0] = x[0].strip()\n", " x[1] = x[1].strip()\n", " df.loc[ind,\"location_state\"] = x[1]\n", " df.loc[ind,\"location_city\"] = x[0]\n", " \n", " job = df[\"job\"][ind]\n", " job = job.replace(\" / \",\"/\")\n", " df.loc[ind,\"job\"] = job\n", " \n", " language = df[\"language\"][ind]\n", " language_json = []\n", " for lang in language.split(\",\"):\n", " lang = lang.replace(\" \",\"\")\n", " lang = lang.replace(\")\",\"\")\n", " lang = lang.strip()\n", " language_and_proficiency = lang.split(\"(\")\n", " if len(language_and_proficiency) < 2:\n", " language_and_proficiency.append(\"fluently\")\n", " if language_and_proficiency[0] not in total_languages:\n", " total_languages.append(language_and_proficiency[0])\n", " if language_and_proficiency[1] not in total_language_proficiency:\n", " total_language_proficiency.append(language_and_proficiency[1])\n", " lan_dict = {}\n", " lan_dict[\"language\"] = language_and_proficiency[0]\n", " lan_dict[\"proficiency\"] = language_and_proficiency[1]\n", " language_json.append(lan_dict)\n", " \n", " df.loc[ind,\"language_json\"] = json.dumps(language_json)\n", " \n", "df.head()" ] }, { "cell_type": "code", "execution_count": 466, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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user_idusernameagestatussexorientationdrinksdrugsheightjoblocationpetssmokeslanguagenew_languagesbody_profileeducation_leveldropped_outbiointerestsother_interestslocation_preferencelikes_dogslikes_catshas_catshas_dogslocation_statelocation_citylanguage_jsonenglish_fluentlyenglish_poorlyenglish_okayspanish_fluentlyspanish_poorlyspanish_okaysignlanguage_fluentlysignlanguage_poorlysignlanguage_okaytagalog_fluentlytagalog_poorlytagalog_okayfrench_fluentlyfrench_poorlyfrench_okayitalian_fluentlyitalian_poorlyitalian_okaygerman_fluentlygerman_poorlygerman_okaychinese_fluentlychinese_poorlychinese_okayc++_fluentlyc++_poorlyc++_okayhebrew_fluentlyhebrew_poorlyhebrew_okayswedish_fluentlyswedish_poorlyswedish_okayhawaiian_fluentlyhawaiian_poorlyhawaiian_okayirish_fluentlyirish_poorlyirish_okayrussian_fluentlyrussian_poorlyrussian_okaykorean_fluentlykorean_poorlykorean_okayarabic_fluentlyarabic_poorlyarabic_okayportuguese_fluentlyportuguese_poorlyportuguese_okayjapanese_fluentlyjapanese_poorlyjapanese_okayother_fluentlyother_poorlyother_okaythai_fluentlythai_poorlythai_okayesperanto_fluentlyesperanto_poorlyesperanto_okaycroatian_fluentlycroatian_poorlycroatian_okayfarsi_fluentlyfarsi_poorlyfarsi_okaygreek_fluentlygreek_poorlygreek_okayukrainian_fluentlyukrainian_poorlyukrainian_okayhindi_fluentlyhindi_poorlyhindi_okaysanskrit_fluentlysanskrit_poorlysanskrit_okayswahili_fluentlyswahili_poorlyswahili_okayicelandic_fluentlyicelandic_poorlyicelandic_okaybengali_fluentlybengali_poorlybengali_okaymaori_fluentlymaori_poorlymaori_okayslovak_fluentlyslovak_poorlyslovak_okaynorwegian_fluentlynorwegian_poorlynorwegian_okaylatin_fluentlylatin_poorlylatin_okayvietnamese_fluentlyvietnamese_poorlyvietnamese_okayczech_fluentlyczech_poorlyczech_okayyiddish_fluentlyyiddish_poorlyyiddish_okayurdu_fluentlyurdu_poorlyurdu_okayturkish_fluentlyturkish_poorlyturkish_okaylisp_fluentlylisp_poorlylisp_okayindonesian_fluentlyindonesian_poorlyindonesian_okayslovenian_fluentlyslovenian_poorlyslovenian_okaypolish_fluentlypolish_poorlypolish_okaydanish_fluentlydanish_poorlydanish_okayancientgreek_fluentlyancientgreek_poorlyancientgreek_okayafrikaans_fluentlyafrikaans_poorlyafrikaans_okaydutch_fluentlydutch_poorlydutch_okayalbanian_fluentlyalbanian_poorlyalbanian_okaybulgarian_fluentlybulgarian_poorlybulgarian_okayfinnish_fluentlyfinnish_poorlyfinnish_okayhungarian_fluentlyhungarian_poorlyhungarian_okaymongolian_fluentlymongolian_poorlymongolian_okaycatalan_fluentlycatalan_poorlycatalan_okaytamil_fluentlytamil_poorlytamil_okaybasque_fluentlybasque_poorlybasque_okaycebuano_fluentlycebuano_poorlycebuano_okaygujarati_fluentlygujarati_poorlygujarati_okayromanian_fluentlyromanian_poorlyromanian_okaytibetan_fluentlytibetan_poorlytibetan_okayarmenian_fluentlyarmenian_poorlyarmenian_okaymalay_fluentlymalay_poorlymalay_okayilongo_fluentlyilongo_poorlyilongo_okaypersian_fluentlypersian_poorlypersian_okay
0fffe3100Edith Lopez27singlefgaysociallynever66.0medicine/healthoakland, californialikes dogs and likes catsnoenglish (fluently), spanish (poorly), sign lan...interestedathletic4.0nobottom line i love life! i work hard and i lov...sportsinstrumentssame stateTrueTrueFalseFalsecaliforniaoakland[{\"language\": \"english\", \"proficiency\": \"fluen...100010010000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
1fffe3200Travis Young26singlemgaysociallynever68.0otherpleasant hill, californialikes dogsnoenglish (fluently), tagalog (okay), french (po...interestedfit3.0noi'm a straightforward, genuine, fun loving (i'...paintinginstrumentsanywhereTrueFalseFalseFalsecaliforniapleasant hill[{\"language\": \"english\", \"proficiency\": \"fluen...100000000001010000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
2fffe3300Agnes Smith20seeing someonefbisexualsociallysometimes69.0otheroakland, californiahas dogs and likes catssometimesenglish (fluently), sign language (poorly), fr...interestedfit2.0nommmmm yummy tacosss. yoga is where it's at. i ...instrumentsdancingsame cityFalseTrueFalseTruecaliforniaoakland[{\"language\": \"english\", \"proficiency\": \"fluen...100000010000010010000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
3fffe3400Salvador Klaver27singlembisexualsociallysometimes68.0computer/hardware/softwaredaly city, californialikes dogs and likes catsnoenglishnot interestedaverage3.0noi'm a stealth geek. that special mix of techni...sketchingactingsame cityTrueTrueFalseFalsecaliforniadaly city[{\"language\": \"english\", \"proficiency\": \"fluen...100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
4fffe3500Elana Sewell22singlefbisexualoftensometimes68.0otheroakland, californialikes dogs and likes catsyesenglishnot interestedaverage2.0yeswith the whisper of the wind i was weaved into...craftdesigningsame cityTrueTrueFalseFalsecaliforniaoakland[{\"language\": \"english\", \"proficiency\": \"fluen...100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
\n", "
" ], "text/plain": [ " user_id username age status sex orientation drinks \\\n", "0 fffe3100 Edith Lopez 27 single f gay socially \n", "1 fffe3200 Travis Young 26 single m gay socially \n", "2 fffe3300 Agnes Smith 20 seeing someone f bisexual socially \n", "3 fffe3400 Salvador Klaver 27 single m bisexual socially \n", "4 fffe3500 Elana Sewell 22 single f bisexual often \n", "\n", " drugs height job location \\\n", "0 never 66.0 medicine/health oakland, california \n", "1 never 68.0 other pleasant hill, california \n", "2 sometimes 69.0 other oakland, california \n", "3 sometimes 68.0 computer/hardware/software daly city, california \n", "4 sometimes 68.0 other oakland, california \n", "\n", " pets smokes \\\n", "0 likes dogs and likes cats no \n", "1 likes dogs no \n", "2 has dogs and likes cats sometimes \n", "3 likes dogs and likes cats no \n", "4 likes dogs and likes cats yes \n", "\n", " language new_languages \\\n", "0 english (fluently), spanish (poorly), sign lan... interested \n", "1 english (fluently), tagalog (okay), french (po... interested \n", "2 english (fluently), sign language (poorly), fr... interested \n", "3 english not interested \n", "4 english not interested \n", "\n", " body_profile education_level dropped_out \\\n", "0 athletic 4.0 no \n", "1 fit 3.0 no \n", "2 fit 2.0 no \n", "3 average 3.0 no \n", "4 average 2.0 yes \n", "\n", " bio interests \\\n", "0 bottom line i love life! i work hard and i lov... sports \n", "1 i'm a straightforward, genuine, fun loving (i'... painting \n", "2 mmmmm yummy tacosss. yoga is where it's at. i ... instruments \n", "3 i'm a stealth geek. that special mix of techni... sketching \n", "4 with the whisper of the wind i was weaved into... craft \n", "\n", " other_interests location_preference likes_dogs likes_cats has_cats \\\n", "0 instruments same state True True False \n", "1 instruments anywhere True False False \n", "2 dancing same city False True False \n", "3 acting same city True True False \n", "4 designing same city True True False \n", "\n", " has_dogs location_state location_city \\\n", "0 False california oakland \n", "1 False california pleasant hill \n", "2 True california oakland \n", "3 False california daly city \n", "4 False california oakland \n", "\n", " language_json english_fluently \\\n", "0 [{\"language\": \"english\", \"proficiency\": \"fluen... 1 \n", "1 [{\"language\": \"english\", \"proficiency\": \"fluen... 1 \n", "2 [{\"language\": \"english\", \"proficiency\": \"fluen... 1 \n", "3 [{\"language\": \"english\", \"proficiency\": \"fluen... 1 \n", "4 [{\"language\": \"english\", \"proficiency\": \"fluen... 1 \n", "\n", " english_poorly english_okay spanish_fluently spanish_poorly \\\n", "0 0 0 0 1 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " spanish_okay signlanguage_fluently signlanguage_poorly \\\n", "0 0 0 1 \n", "1 0 0 0 \n", "2 0 0 1 \n", "3 0 0 0 \n", "4 0 0 0 \n", "\n", " signlanguage_okay tagalog_fluently tagalog_poorly tagalog_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 1 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " french_fluently french_poorly french_okay italian_fluently \\\n", "0 0 0 0 0 \n", "1 0 1 0 0 \n", "2 0 1 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " italian_poorly italian_okay german_fluently german_poorly german_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 1 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " chinese_fluently chinese_poorly chinese_okay c++_fluently c++_poorly \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " c++_okay hebrew_fluently hebrew_poorly hebrew_okay swedish_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " swedish_poorly swedish_okay hawaiian_fluently hawaiian_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " hawaiian_okay irish_fluently irish_poorly irish_okay russian_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " russian_poorly russian_okay korean_fluently korean_poorly korean_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " arabic_fluently arabic_poorly arabic_okay portuguese_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " portuguese_poorly portuguese_okay japanese_fluently japanese_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " japanese_okay other_fluently other_poorly other_okay thai_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " thai_poorly thai_okay esperanto_fluently esperanto_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " esperanto_okay croatian_fluently croatian_poorly croatian_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " farsi_fluently farsi_poorly farsi_okay greek_fluently greek_poorly \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " greek_okay ukrainian_fluently ukrainian_poorly ukrainian_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " hindi_fluently hindi_poorly hindi_okay sanskrit_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " sanskrit_poorly sanskrit_okay swahili_fluently swahili_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " swahili_okay icelandic_fluently icelandic_poorly icelandic_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " bengali_fluently bengali_poorly bengali_okay maori_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " maori_poorly maori_okay slovak_fluently slovak_poorly slovak_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " norwegian_fluently norwegian_poorly norwegian_okay latin_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " latin_poorly latin_okay vietnamese_fluently vietnamese_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " vietnamese_okay czech_fluently czech_poorly czech_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " yiddish_fluently yiddish_poorly yiddish_okay urdu_fluently urdu_poorly \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " urdu_okay turkish_fluently turkish_poorly turkish_okay lisp_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " lisp_poorly lisp_okay indonesian_fluently indonesian_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " indonesian_okay slovenian_fluently slovenian_poorly slovenian_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " polish_fluently polish_poorly polish_okay danish_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " danish_poorly danish_okay ancientgreek_fluently ancientgreek_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " ancientgreek_okay afrikaans_fluently afrikaans_poorly afrikaans_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " dutch_fluently dutch_poorly dutch_okay albanian_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " albanian_poorly albanian_okay bulgarian_fluently bulgarian_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " bulgarian_okay finnish_fluently finnish_poorly finnish_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " hungarian_fluently hungarian_poorly hungarian_okay mongolian_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " mongolian_poorly mongolian_okay catalan_fluently catalan_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " catalan_okay tamil_fluently tamil_poorly tamil_okay basque_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " basque_poorly basque_okay cebuano_fluently cebuano_poorly cebuano_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " gujarati_fluently gujarati_poorly gujarati_okay romanian_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " romanian_poorly romanian_okay tibetan_fluently tibetan_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " tibetan_okay armenian_fluently armenian_poorly armenian_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " malay_fluently malay_poorly malay_okay ilongo_fluently ilongo_poorly \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " ilongo_okay persian_fluently persian_poorly persian_okay \n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 " ] }, "execution_count": 466, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lang_columns = []\n", "for lang in total_languages:\n", " for prof in total_language_proficiency:\n", " lang_columns.append(lang +\"_\"+ prof)\n", "df_language = pd.DataFrame(columns=lang_columns)\n", "\n", "for ind in df.index:\n", " language_json = df[\"language_json\"][ind]\n", " language_json = json.loads(language_json)\n", " for lan in language_json:\n", " column_name = lan[\"language\"] + \"_\" + lan[\"proficiency\"]\n", " df_language.loc[ind,column_name] = 1\n", " \n", "df_language = df_language.fillna(0)\n", "# df_language.head()\n", "df = pd.concat([df,df_language],axis=1)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 467, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "california 1997\n", "vietnam 1\n", "ohio 1\n", "arizona 1\n", "new york 1\n", "Name: location_state, dtype: int64\n", "--------------------------------------------------\n", "san francisco 911\n", "oakland 250\n", "berkeley 146\n", "san mateo 56\n", "palo alto 45\n", " ... \n", "brisbane 1\n", "cincinnati 1\n", "montara 1\n", "point richmond 1\n", "canyon country 1\n", "Name: location_city, Length: 70, dtype: int64\n" ] } ], "source": [ "print(df[\"location_state\"].value_counts())\n", "print(\"-\"*50)\n", "print(df[\"location_city\"].value_counts())" ] }, { "cell_type": "code", "execution_count": 468, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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0fffe3100Edith Lopez273014066.0medicine/healthoakland, californialikes dogs and likes cats0english (fluently), spanish (poorly), sign lan...014.00bottom line i love life! i work hard and i lov...251321100140[{\"language\": \"english\", \"proficiency\": \"fluen...100010010000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
1fffe3200Travis Young263114068.0otherpleasant hill, californialikes dogs0english (fluently), tagalog (okay), french (po...043.00i'm a straightforward, genuine, fun loving (i'...181301000147[{\"language\": \"english\", \"proficiency\": \"fluen...100000000001010000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
2fffe3300Agnes Smith202004269.0otheroakland, californiahas dogs and likes cats1english (fluently), sign language (poorly), fr...042.00mmmmm yummy tacosss. yoga is where it's at. i ...13710101140[{\"language\": \"english\", \"proficiency\": \"fluen...100000010000010010000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
3fffe3400Salvador Klaver273104268.0computer/hardware/softwaredaly city, californialikes dogs and likes cats0english123.00i'm a stealth geek. that special mix of techni...23011100114[{\"language\": \"english\", \"proficiency\": \"fluen...100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
4fffe3500Elana Sewell223002268.0otheroakland, californialikes dogs and likes cats4english122.01with the whisper of the wind i was weaved into...6811100140[{\"language\": \"english\", \"proficiency\": \"fluen...100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
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" ], "text/plain": [ " user_id username age status sex orientation drinks drugs \\\n", "0 fffe3100 Edith Lopez 27 3 0 1 4 0 \n", "1 fffe3200 Travis Young 26 3 1 1 4 0 \n", "2 fffe3300 Agnes Smith 20 2 0 0 4 2 \n", "3 fffe3400 Salvador Klaver 27 3 1 0 4 2 \n", "4 fffe3500 Elana Sewell 22 3 0 0 2 2 \n", "\n", " height job location \\\n", "0 66.0 medicine/health oakland, california \n", "1 68.0 other pleasant hill, california \n", "2 69.0 other oakland, california \n", "3 68.0 computer/hardware/software daly city, california \n", "4 68.0 other oakland, california \n", "\n", " pets smokes \\\n", "0 likes dogs and likes cats 0 \n", "1 likes dogs 0 \n", "2 has dogs and likes cats 1 \n", "3 likes dogs and likes cats 0 \n", "4 likes dogs and likes cats 4 \n", "\n", " language new_languages \\\n", "0 english (fluently), spanish (poorly), sign lan... 0 \n", "1 english (fluently), tagalog (okay), french (po... 0 \n", "2 english (fluently), sign language (poorly), fr... 0 \n", "3 english 1 \n", "4 english 1 \n", "\n", " body_profile education_level dropped_out \\\n", "0 1 4.0 0 \n", "1 4 3.0 0 \n", "2 4 2.0 0 \n", "3 2 3.0 0 \n", "4 2 2.0 1 \n", "\n", " bio interests \\\n", "0 bottom line i love life! i work hard and i lov... 25 \n", "1 i'm a straightforward, genuine, fun loving (i'... 18 \n", "2 mmmmm yummy tacosss. yoga is where it's at. i ... 13 \n", "3 i'm a stealth geek. that special mix of techni... 23 \n", "4 with the whisper of the wind i was weaved into... 6 \n", "\n", " other_interests location_preference likes_dogs likes_cats has_cats \\\n", "0 13 2 1 1 0 \n", "1 13 0 1 0 0 \n", "2 7 1 0 1 0 \n", "3 0 1 1 1 0 \n", "4 8 1 1 1 0 \n", "\n", " has_dogs location_state location_city \\\n", "0 0 1 40 \n", "1 0 1 47 \n", "2 1 1 40 \n", "3 0 1 14 \n", "4 0 1 40 \n", "\n", " language_json english_fluently \\\n", "0 [{\"language\": \"english\", \"proficiency\": \"fluen... 1 \n", "1 [{\"language\": \"english\", \"proficiency\": \"fluen... 1 \n", "2 [{\"language\": \"english\", \"proficiency\": \"fluen... 1 \n", "3 [{\"language\": \"english\", \"proficiency\": \"fluen... 1 \n", "4 [{\"language\": \"english\", \"proficiency\": \"fluen... 1 \n", "\n", " english_poorly english_okay spanish_fluently spanish_poorly \\\n", "0 0 0 0 1 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " spanish_okay signlanguage_fluently signlanguage_poorly \\\n", "0 0 0 1 \n", "1 0 0 0 \n", "2 0 0 1 \n", "3 0 0 0 \n", "4 0 0 0 \n", "\n", " signlanguage_okay tagalog_fluently tagalog_poorly tagalog_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 1 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " french_fluently french_poorly french_okay italian_fluently \\\n", "0 0 0 0 0 \n", "1 0 1 0 0 \n", "2 0 1 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " italian_poorly italian_okay german_fluently german_poorly german_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 1 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " chinese_fluently chinese_poorly chinese_okay c++_fluently c++_poorly \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " c++_okay hebrew_fluently hebrew_poorly hebrew_okay swedish_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " swedish_poorly swedish_okay hawaiian_fluently hawaiian_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " hawaiian_okay irish_fluently irish_poorly irish_okay russian_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " russian_poorly russian_okay korean_fluently korean_poorly korean_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " arabic_fluently arabic_poorly arabic_okay portuguese_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " portuguese_poorly portuguese_okay japanese_fluently japanese_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " japanese_okay other_fluently other_poorly other_okay thai_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " thai_poorly thai_okay esperanto_fluently esperanto_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " esperanto_okay croatian_fluently croatian_poorly croatian_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " farsi_fluently farsi_poorly farsi_okay greek_fluently greek_poorly \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " greek_okay ukrainian_fluently ukrainian_poorly ukrainian_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " hindi_fluently hindi_poorly hindi_okay sanskrit_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " sanskrit_poorly sanskrit_okay swahili_fluently swahili_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " swahili_okay icelandic_fluently icelandic_poorly icelandic_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " bengali_fluently bengali_poorly bengali_okay maori_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " maori_poorly maori_okay slovak_fluently slovak_poorly slovak_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " norwegian_fluently norwegian_poorly norwegian_okay latin_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " latin_poorly latin_okay vietnamese_fluently vietnamese_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " vietnamese_okay czech_fluently czech_poorly czech_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " yiddish_fluently yiddish_poorly yiddish_okay urdu_fluently urdu_poorly \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " urdu_okay turkish_fluently turkish_poorly turkish_okay lisp_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " lisp_poorly lisp_okay indonesian_fluently indonesian_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " indonesian_okay slovenian_fluently slovenian_poorly slovenian_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " polish_fluently polish_poorly polish_okay danish_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " danish_poorly danish_okay ancientgreek_fluently ancientgreek_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " ancientgreek_okay afrikaans_fluently afrikaans_poorly afrikaans_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " dutch_fluently dutch_poorly dutch_okay albanian_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " albanian_poorly albanian_okay bulgarian_fluently bulgarian_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " bulgarian_okay finnish_fluently finnish_poorly finnish_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " hungarian_fluently hungarian_poorly hungarian_okay mongolian_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " mongolian_poorly mongolian_okay catalan_fluently catalan_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " catalan_okay tamil_fluently tamil_poorly tamil_okay basque_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " basque_poorly basque_okay cebuano_fluently cebuano_poorly cebuano_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " gujarati_fluently gujarati_poorly gujarati_okay romanian_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " romanian_poorly romanian_okay tibetan_fluently tibetan_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " tibetan_okay armenian_fluently armenian_poorly armenian_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " malay_fluently malay_poorly malay_okay ilongo_fluently ilongo_poorly \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " ilongo_okay persian_fluently persian_poorly persian_okay \n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 " ] }, "execution_count": 468, "metadata": {}, "output_type": "execute_result" } ], "source": [ "label_object = {}\n", "categorical_columns = [\n", " 'status',\n", " 'sex',\n", " 'orientation',\n", " 'drinks',\n", " 'drugs',\n", " 'smokes',\n", " 'new_languages',\n", " 'body_profile',\n", " 'dropped_out',\n", " 'interests',\n", " 'other_interests',\n", " 'location_preference',\n", " 'likes_dogs',\n", " 'likes_cats',\n", " 'has_cats',\n", " 'has_dogs',\n", " 'location_state',\n", " 'location_city'\n", "]\n", "for col in categorical_columns:\n", " labelencoder = LabelEncoder()\n", " labelencoder.fit(df[col])\n", " df[col] = labelencoder.fit_transform(df[col])\n", " label_object[col] = labelencoder\n", " \n", "df.head()" ] }, { "cell_type": "code", "execution_count": 469, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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0fffe3100Edith Lopez273014066.0medicine/healthoakland, californialikes dogs and likes cats0english (fluently), spanish (poorly), sign lan...014.00bottom line i love life! i work hard and i lov...251321100140[{\"language\": \"english\", \"proficiency\": \"fluen...1000100100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100000010000000000000000
1fffe3200Travis Young263114068.0otherpleasant hill, californialikes dogs0english (fluently), tagalog (okay), french (po...043.00i'm a straightforward, genuine, fun loving (i'...181301000147[{\"language\": \"english\", \"proficiency\": \"fluen...1000000000010100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010000000000000
2fffe3300Agnes Smith202004269.0otheroakland, californiahas dogs and likes cats1english (fluently), sign language (poorly), fr...042.00mmmmm yummy tacosss. yoga is where it's at. i ...13710101140[{\"language\": \"english\", \"proficiency\": \"fluen...1000000100000100100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010000000000000
3fffe3400Salvador Klaver273104268.0computer/hardware/softwaredaly city, californialikes dogs and likes cats0english123.00i'm a stealth geek. that special mix of techni...23011100114[{\"language\": \"english\", \"proficiency\": \"fluen...1000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001000000001000000000000000001000000
4fffe3500Elana Sewell223002268.0otheroakland, californialikes dogs and likes cats4english122.01with the whisper of the wind i was weaved into...6811100140[{\"language\": \"english\", \"proficiency\": \"fluen...1000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010000000000000
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" ], "text/plain": [ " user_id username age status sex orientation drinks drugs \\\n", "0 fffe3100 Edith Lopez 27 3 0 1 4 0 \n", "1 fffe3200 Travis Young 26 3 1 1 4 0 \n", "2 fffe3300 Agnes Smith 20 2 0 0 4 2 \n", "3 fffe3400 Salvador Klaver 27 3 1 0 4 2 \n", "4 fffe3500 Elana Sewell 22 3 0 0 2 2 \n", "\n", " height job location \\\n", "0 66.0 medicine/health oakland, california \n", "1 68.0 other pleasant hill, california \n", "2 69.0 other oakland, california \n", "3 68.0 computer/hardware/software daly city, california \n", "4 68.0 other oakland, california \n", "\n", " pets smokes \\\n", "0 likes dogs and likes cats 0 \n", "1 likes dogs 0 \n", "2 has dogs and likes cats 1 \n", "3 likes dogs and likes cats 0 \n", "4 likes dogs and likes cats 4 \n", "\n", " language new_languages \\\n", "0 english (fluently), spanish (poorly), sign lan... 0 \n", "1 english (fluently), tagalog (okay), french (po... 0 \n", "2 english (fluently), sign language (poorly), fr... 0 \n", "3 english 1 \n", "4 english 1 \n", "\n", " body_profile education_level dropped_out \\\n", "0 1 4.0 0 \n", "1 4 3.0 0 \n", "2 4 2.0 0 \n", "3 2 3.0 0 \n", "4 2 2.0 1 \n", "\n", " bio interests \\\n", "0 bottom line i love life! i work hard and i lov... 25 \n", "1 i'm a straightforward, genuine, fun loving (i'... 18 \n", "2 mmmmm yummy tacosss. yoga is where it's at. i ... 13 \n", "3 i'm a stealth geek. that special mix of techni... 23 \n", "4 with the whisper of the wind i was weaved into... 6 \n", "\n", " other_interests location_preference likes_dogs likes_cats has_cats \\\n", "0 13 2 1 1 0 \n", "1 13 0 1 0 0 \n", "2 7 1 0 1 0 \n", "3 0 1 1 1 0 \n", "4 8 1 1 1 0 \n", "\n", " has_dogs location_state location_city \\\n", "0 0 1 40 \n", "1 0 1 47 \n", "2 1 1 40 \n", "3 0 1 14 \n", "4 0 1 40 \n", "\n", " language_json english_fluently \\\n", "0 [{\"language\": \"english\", \"proficiency\": \"fluen... 1 \n", "1 [{\"language\": \"english\", \"proficiency\": \"fluen... 1 \n", "2 [{\"language\": \"english\", \"proficiency\": \"fluen... 1 \n", "3 [{\"language\": \"english\", \"proficiency\": \"fluen... 1 \n", "4 [{\"language\": \"english\", \"proficiency\": \"fluen... 1 \n", "\n", " english_poorly english_okay spanish_fluently spanish_poorly \\\n", "0 0 0 0 1 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " spanish_okay signlanguage_fluently signlanguage_poorly \\\n", "0 0 0 1 \n", "1 0 0 0 \n", "2 0 0 1 \n", "3 0 0 0 \n", "4 0 0 0 \n", "\n", " signlanguage_okay tagalog_fluently tagalog_poorly tagalog_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 1 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " french_fluently french_poorly french_okay italian_fluently \\\n", "0 0 0 0 0 \n", "1 0 1 0 0 \n", "2 0 1 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " italian_poorly italian_okay german_fluently german_poorly german_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 1 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " chinese_fluently chinese_poorly chinese_okay c++_fluently c++_poorly \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " c++_okay hebrew_fluently hebrew_poorly hebrew_okay swedish_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " swedish_poorly swedish_okay hawaiian_fluently hawaiian_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " hawaiian_okay irish_fluently irish_poorly irish_okay russian_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " russian_poorly russian_okay korean_fluently korean_poorly korean_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " arabic_fluently arabic_poorly arabic_okay portuguese_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " portuguese_poorly portuguese_okay japanese_fluently japanese_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " japanese_okay other_fluently other_poorly other_okay thai_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " thai_poorly thai_okay esperanto_fluently esperanto_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " esperanto_okay croatian_fluently croatian_poorly croatian_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " farsi_fluently farsi_poorly farsi_okay greek_fluently greek_poorly \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " greek_okay ukrainian_fluently ukrainian_poorly ukrainian_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " hindi_fluently hindi_poorly hindi_okay sanskrit_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " sanskrit_poorly sanskrit_okay swahili_fluently swahili_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " swahili_okay icelandic_fluently icelandic_poorly icelandic_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " bengali_fluently bengali_poorly bengali_okay maori_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " maori_poorly maori_okay slovak_fluently slovak_poorly slovak_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " norwegian_fluently norwegian_poorly norwegian_okay latin_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " latin_poorly latin_okay vietnamese_fluently vietnamese_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " vietnamese_okay czech_fluently czech_poorly czech_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " yiddish_fluently yiddish_poorly yiddish_okay urdu_fluently urdu_poorly \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " urdu_okay turkish_fluently turkish_poorly turkish_okay lisp_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " lisp_poorly lisp_okay indonesian_fluently indonesian_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " indonesian_okay slovenian_fluently slovenian_poorly slovenian_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " polish_fluently polish_poorly polish_okay danish_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " danish_poorly danish_okay ancientgreek_fluently ancientgreek_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " ancientgreek_okay afrikaans_fluently afrikaans_poorly afrikaans_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " dutch_fluently dutch_poorly dutch_okay albanian_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " albanian_poorly albanian_okay bulgarian_fluently bulgarian_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " bulgarian_okay finnish_fluently finnish_poorly finnish_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " hungarian_fluently hungarian_poorly hungarian_okay mongolian_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " mongolian_poorly mongolian_okay catalan_fluently catalan_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " catalan_okay tamil_fluently tamil_poorly tamil_okay basque_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " basque_poorly basque_okay cebuano_fluently cebuano_poorly cebuano_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " gujarati_fluently gujarati_poorly gujarati_okay romanian_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " romanian_poorly romanian_okay tibetan_fluently tibetan_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " tibetan_okay armenian_fluently armenian_poorly armenian_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " malay_fluently malay_poorly malay_okay ilongo_fluently ilongo_poorly \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " ilongo_okay persian_fluently persian_poorly persian_okay job_academia \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " job_administrative job_artistic job_banking job_biz dev job_clerical \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " job_computer job_construction job_craftsmanship job_education \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 1 0 0 0 \n", "4 0 0 0 0 \n", "\n", " job_engineering job_entertainment job_executive job_financial \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " job_government job_hardware job_health job_hospitality job_law \\\n", "0 0 0 1 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 1 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " job_legal services job_management job_marketing job_media job_medicine \\\n", "0 0 0 0 0 1 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " job_military job_musical job_other job_political job_rather not say \\\n", "0 0 0 0 0 0 \n", "1 0 0 1 0 0 \n", "2 0 0 1 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 1 0 0 \n", "\n", " job_real estate job_retired job_sales job_science job_software \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 1 \n", "4 0 0 0 0 0 \n", "\n", " job_student job_tech job_transportation job_travel job_unemployed \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " job_writer \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 " ] }, "execution_count": 469, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cleaned = df.job.str.split('/', expand=True).stack()\n", "job_df = pd.get_dummies(cleaned,prefix=\"job\").groupby(level=0).sum()\n", "df = pd.concat([df,job_df],axis=1)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 470, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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0fffe3100273014066.00014.00bottom line i love life! i work hard and i lov...2513211001401000100100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100000010000000000000000
1fffe3200263114068.00043.00i'm a straightforward, genuine, fun loving (i'...1813010001471000000000010100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010000000000000
2fffe3300202004269.01042.00mmmmm yummy tacosss. yoga is where it's at. i ...137101011401000000100000100100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010000000000000
3fffe3400273104268.00123.00i'm a stealth geek. that special mix of techni...230111001141000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001000000001000000000000000001000000
4fffe3500223002268.04122.01with the whisper of the wind i was weaved into...68111001401000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010000000000000
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" ], "text/plain": [ " user_id age status sex orientation drinks drugs height smokes \\\n", "0 fffe3100 27 3 0 1 4 0 66.0 0 \n", "1 fffe3200 26 3 1 1 4 0 68.0 0 \n", "2 fffe3300 20 2 0 0 4 2 69.0 1 \n", "3 fffe3400 27 3 1 0 4 2 68.0 0 \n", "4 fffe3500 22 3 0 0 2 2 68.0 4 \n", "\n", " new_languages body_profile education_level dropped_out \\\n", "0 0 1 4.0 0 \n", "1 0 4 3.0 0 \n", "2 0 4 2.0 0 \n", "3 1 2 3.0 0 \n", "4 1 2 2.0 1 \n", "\n", " bio interests \\\n", "0 bottom line i love life! i work hard and i lov... 25 \n", "1 i'm a straightforward, genuine, fun loving (i'... 18 \n", "2 mmmmm yummy tacosss. yoga is where it's at. i ... 13 \n", "3 i'm a stealth geek. that special mix of techni... 23 \n", "4 with the whisper of the wind i was weaved into... 6 \n", "\n", " other_interests location_preference likes_dogs likes_cats has_cats \\\n", "0 13 2 1 1 0 \n", "1 13 0 1 0 0 \n", "2 7 1 0 1 0 \n", "3 0 1 1 1 0 \n", "4 8 1 1 1 0 \n", "\n", " has_dogs location_state location_city english_fluently english_poorly \\\n", "0 0 1 40 1 0 \n", "1 0 1 47 1 0 \n", "2 1 1 40 1 0 \n", "3 0 1 14 1 0 \n", "4 0 1 40 1 0 \n", "\n", " english_okay spanish_fluently spanish_poorly spanish_okay \\\n", "0 0 0 1 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " signlanguage_fluently signlanguage_poorly signlanguage_okay \\\n", "0 0 1 0 \n", "1 0 0 0 \n", "2 0 1 0 \n", "3 0 0 0 \n", "4 0 0 0 \n", "\n", " tagalog_fluently tagalog_poorly tagalog_okay french_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 1 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " french_poorly french_okay italian_fluently italian_poorly italian_okay \\\n", "0 0 0 0 0 0 \n", "1 1 0 0 0 0 \n", "2 1 0 0 1 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " german_fluently german_poorly german_okay chinese_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " chinese_poorly chinese_okay c++_fluently c++_poorly c++_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " hebrew_fluently hebrew_poorly hebrew_okay swedish_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " swedish_poorly swedish_okay hawaiian_fluently hawaiian_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " hawaiian_okay irish_fluently irish_poorly irish_okay russian_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " russian_poorly russian_okay korean_fluently korean_poorly korean_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " arabic_fluently arabic_poorly arabic_okay portuguese_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " portuguese_poorly portuguese_okay japanese_fluently japanese_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " japanese_okay other_fluently other_poorly other_okay thai_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " thai_poorly thai_okay esperanto_fluently esperanto_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " esperanto_okay croatian_fluently croatian_poorly croatian_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " farsi_fluently farsi_poorly farsi_okay greek_fluently greek_poorly \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " greek_okay ukrainian_fluently ukrainian_poorly ukrainian_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " hindi_fluently hindi_poorly hindi_okay sanskrit_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " sanskrit_poorly sanskrit_okay swahili_fluently swahili_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " swahili_okay icelandic_fluently icelandic_poorly icelandic_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " bengali_fluently bengali_poorly bengali_okay maori_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " maori_poorly maori_okay slovak_fluently slovak_poorly slovak_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " norwegian_fluently norwegian_poorly norwegian_okay latin_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " latin_poorly latin_okay vietnamese_fluently vietnamese_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " vietnamese_okay czech_fluently czech_poorly czech_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " yiddish_fluently yiddish_poorly yiddish_okay urdu_fluently urdu_poorly \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " urdu_okay turkish_fluently turkish_poorly turkish_okay lisp_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " lisp_poorly lisp_okay indonesian_fluently indonesian_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " indonesian_okay slovenian_fluently slovenian_poorly slovenian_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " polish_fluently polish_poorly polish_okay danish_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " danish_poorly danish_okay ancientgreek_fluently ancientgreek_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " ancientgreek_okay afrikaans_fluently afrikaans_poorly afrikaans_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " dutch_fluently dutch_poorly dutch_okay albanian_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " albanian_poorly albanian_okay bulgarian_fluently bulgarian_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " bulgarian_okay finnish_fluently finnish_poorly finnish_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " hungarian_fluently hungarian_poorly hungarian_okay mongolian_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " mongolian_poorly mongolian_okay catalan_fluently catalan_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " catalan_okay tamil_fluently tamil_poorly tamil_okay basque_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " basque_poorly basque_okay cebuano_fluently cebuano_poorly cebuano_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " gujarati_fluently gujarati_poorly gujarati_okay romanian_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " romanian_poorly romanian_okay tibetan_fluently tibetan_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " tibetan_okay armenian_fluently armenian_poorly armenian_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " malay_fluently malay_poorly malay_okay ilongo_fluently ilongo_poorly \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " ilongo_okay persian_fluently persian_poorly persian_okay job_academia \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " job_administrative job_artistic job_banking job_biz dev job_clerical \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " job_computer job_construction job_craftsmanship job_education \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 1 0 0 0 \n", "4 0 0 0 0 \n", "\n", " job_engineering job_entertainment job_executive job_financial \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " job_government job_hardware job_health job_hospitality job_law \\\n", "0 0 0 1 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 1 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " job_legal services job_management job_marketing job_media job_medicine \\\n", "0 0 0 0 0 1 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " job_military job_musical job_other job_political job_rather not say \\\n", "0 0 0 0 0 0 \n", "1 0 0 1 0 0 \n", "2 0 0 1 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 1 0 0 \n", "\n", " job_real estate job_retired job_sales job_science job_software \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 1 \n", "4 0 0 0 0 0 \n", "\n", " job_student job_tech job_transportation job_travel job_unemployed \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " job_writer \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 " ] }, "execution_count": 470, "metadata": {}, "output_type": "execute_result" } ], "source": [ "redundant_columns = ['username','job','location','pets','language','language_json']\n", "df = df.drop(redundant_columns,axis=1)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 471, "metadata": {}, "outputs": [ { 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0fffe310027066.04.00bottom line i love life! i work hard and i lov...110010001001000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001000000100000000000000000.00.00.01.00.01.00.00.00.00.00.01.00.01.00.00.01.00.00.00.00.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
1fffe320026168.03.00i'm a straightforward, genuine, fun loving (i'...100010000000000101000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100000000000000.00.00.01.00.01.00.00.00.00.00.01.00.01.00.00.01.00.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
2fffe330020069.02.00mmmmm yummy tacosss. yoga is where it's at. i ...010110000001000001001000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100000000000000.00.01.00.01.00.00.00.00.00.00.01.00.00.00.01.00.01.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
3fffe340027168.03.00i'm a stealth geek. that special mix of techni...110010000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010000000010000000000000000010000000.00.00.01.01.00.00.00.00.00.00.01.00.00.00.01.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
4fffe350022068.02.01with the whisper of the wind i was weaved into...110010000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100000000000000.00.00.01.01.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.01.00.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
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" ], "text/plain": [ " user_id age sex height education_level dropped_out \\\n", "0 fffe3100 27 0 66.0 4.0 0 \n", "1 fffe3200 26 1 68.0 3.0 0 \n", "2 fffe3300 20 0 69.0 2.0 0 \n", "3 fffe3400 27 1 68.0 3.0 0 \n", "4 fffe3500 22 0 68.0 2.0 1 \n", "\n", " bio likes_dogs likes_cats \\\n", "0 bottom line i love life! i work hard and i lov... 1 1 \n", "1 i'm a straightforward, genuine, fun loving (i'... 1 0 \n", "2 mmmmm yummy tacosss. yoga is where it's at. i ... 0 1 \n", "3 i'm a stealth geek. that special mix of techni... 1 1 \n", "4 with the whisper of the wind i was weaved into... 1 1 \n", "\n", " has_cats has_dogs english_fluently english_poorly english_okay \\\n", "0 0 0 1 0 0 \n", "1 0 0 1 0 0 \n", "2 0 1 1 0 0 \n", "3 0 0 1 0 0 \n", "4 0 0 1 0 0 \n", "\n", " spanish_fluently spanish_poorly spanish_okay signlanguage_fluently \\\n", "0 0 1 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " signlanguage_poorly signlanguage_okay tagalog_fluently tagalog_poorly \\\n", "0 1 0 0 0 \n", "1 0 0 0 0 \n", "2 1 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " tagalog_okay french_fluently french_poorly french_okay \\\n", "0 0 0 0 0 \n", "1 1 0 1 0 \n", "2 0 0 1 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " italian_fluently italian_poorly italian_okay german_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 1 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " german_poorly german_okay chinese_fluently chinese_poorly chinese_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " c++_fluently c++_poorly c++_okay hebrew_fluently hebrew_poorly \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " hebrew_okay swedish_fluently swedish_poorly swedish_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " hawaiian_fluently hawaiian_poorly hawaiian_okay irish_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " irish_poorly irish_okay russian_fluently russian_poorly russian_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " korean_fluently korean_poorly korean_okay arabic_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " arabic_poorly arabic_okay portuguese_fluently portuguese_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " portuguese_okay japanese_fluently japanese_poorly japanese_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " other_fluently other_poorly other_okay thai_fluently thai_poorly \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " thai_okay esperanto_fluently esperanto_poorly esperanto_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " croatian_fluently croatian_poorly croatian_okay farsi_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " farsi_poorly farsi_okay greek_fluently greek_poorly greek_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " ukrainian_fluently ukrainian_poorly ukrainian_okay hindi_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " hindi_poorly hindi_okay sanskrit_fluently sanskrit_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " sanskrit_okay swahili_fluently swahili_poorly swahili_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " icelandic_fluently icelandic_poorly icelandic_okay bengali_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " bengali_poorly bengali_okay maori_fluently maori_poorly maori_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " slovak_fluently slovak_poorly slovak_okay norwegian_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " norwegian_poorly norwegian_okay latin_fluently latin_poorly latin_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " vietnamese_fluently vietnamese_poorly vietnamese_okay czech_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " czech_poorly czech_okay yiddish_fluently yiddish_poorly yiddish_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " urdu_fluently urdu_poorly urdu_okay turkish_fluently turkish_poorly \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " turkish_okay lisp_fluently lisp_poorly lisp_okay indonesian_fluently \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " indonesian_poorly indonesian_okay slovenian_fluently slovenian_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " slovenian_okay polish_fluently polish_poorly polish_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " danish_fluently danish_poorly danish_okay ancientgreek_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " ancientgreek_poorly ancientgreek_okay afrikaans_fluently \\\n", "0 0 0 0 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 0 0 0 \n", "\n", " afrikaans_poorly afrikaans_okay dutch_fluently dutch_poorly dutch_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " albanian_fluently albanian_poorly albanian_okay bulgarian_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " bulgarian_poorly bulgarian_okay finnish_fluently finnish_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " finnish_okay hungarian_fluently hungarian_poorly hungarian_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " mongolian_fluently mongolian_poorly mongolian_okay catalan_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " catalan_poorly catalan_okay tamil_fluently tamil_poorly tamil_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " basque_fluently basque_poorly basque_okay cebuano_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " cebuano_poorly cebuano_okay gujarati_fluently gujarati_poorly \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " gujarati_okay romanian_fluently romanian_poorly romanian_okay \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " tibetan_fluently tibetan_poorly tibetan_okay armenian_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " armenian_poorly armenian_okay malay_fluently malay_poorly malay_okay \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " ilongo_fluently ilongo_poorly ilongo_okay persian_fluently \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " persian_poorly persian_okay job_academia job_administrative \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " job_artistic job_banking job_biz dev job_clerical job_computer \\\n", "0 0 0 0 0 0 \n", "1 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location_state_0 location_state_1 \\\n", "0 1.0 0.0 1.0 \n", "1 0.0 0.0 1.0 \n", "2 0.0 0.0 1.0 \n", "3 0.0 0.0 1.0 \n", "4 0.0 0.0 1.0 \n", "\n", " location_state_2 location_state_3 location_state_4 location_city_0 \\\n", "0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "\n", " location_city_1 location_city_2 location_city_3 location_city_4 \\\n", "0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "\n", " location_city_5 location_city_6 location_city_7 location_city_8 \\\n", "0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "\n", " location_city_9 location_city_10 location_city_11 location_city_12 \\\n", "0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "\n", " location_city_13 location_city_14 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location_city_34 location_city_35 location_city_36 \\\n", "0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "\n", " location_city_37 location_city_38 location_city_39 location_city_40 \\\n", "0 0.0 0.0 0.0 1.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 1.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 1.0 \n", "\n", " location_city_41 location_city_42 location_city_43 location_city_44 \\\n", "0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "\n", " location_city_45 location_city_46 location_city_47 location_city_48 \\\n", "0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 1.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "\n", " location_city_49 location_city_50 location_city_51 location_city_52 \\\n", "0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "\n", " location_city_53 location_city_54 location_city_55 location_city_56 \\\n", "0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "\n", " location_city_57 location_city_58 location_city_59 location_city_60 \\\n", "0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "\n", " location_city_61 location_city_62 location_city_63 location_city_64 \\\n", "0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "\n", " location_city_65 location_city_66 location_city_67 location_city_68 \\\n", "0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "\n", " location_city_69 \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 " ] }, "execution_count": 471, "metadata": {}, "output_type": "execute_result" } ], "source": [ "one_hot_object = {}\n", "categorical_columns = [\n", " 'status',\n", " 'orientation',\n", " 'drinks',\n", " 'drugs',\n", " 'smokes',\n", " 'new_languages',\n", " 'body_profile',\n", " 'interests',\n", " 'other_interests',\n", " 'location_preference',\n", " 'location_state',\n", " 'location_city'\n", "]\n", "for col in categorical_columns:\n", " onehot_encoder = OneHotEncoder()\n", " col_data = df[col].values.reshape(-1,1)\n", " onehot_encoder.fit(col_data)\n", " onehot_encoded = onehot_encoder.fit_transform(col_data).toarray()\n", " columns_list = []\n", " for i in range(onehot_encoded.shape[1]):\n", " columns_list.append(col+\"_\"+str(int(i)))\n", " dfOneHot = pd.DataFrame(onehot_encoded, columns = columns_list) \n", " df = pd.concat([df, dfOneHot], axis=1)\n", " one_hot_object[col] = onehot_encoder\n", " \n", "df = df.drop(categorical_columns,axis=1)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 472, "metadata": {}, "outputs": [], "source": [ "import nltk\n", "from nltk import word_tokenize\n", "from nltk.corpus import stopwords\n", "from unidecode import unidecode\n", "import string\n", "from nltk.stem import WordNetLemmatizer\n", "from nltk.tokenize import word_tokenize\n", "from nltk.corpus import wordnet\n", "from nltk.stem import PorterStemmer\n", "from nltk.tokenize import RegexpTokenizer\n", "\n", "def get_wordnet_pos(word):\n", " \"\"\"Map POS tag to first character lemmatize() accepts\"\"\"\n", " tag = nltk.pos_tag([word])[0][1][0].upper()\n", " tag_dict = {\"J\": wordnet.ADJ,\n", " \"N\": wordnet.NOUN,\n", " \"V\": wordnet.VERB,\n", " \"R\": wordnet.ADV}\n", "\n", " return tag_dict.get(tag, wordnet.NOUN)\n", "\n", "def pre_process(corpus):\n", " # convert input corpus to lower case.\n", " corpus = corpus.lower()\n", " # collecting a list of stop words from nltk and punctuation form\n", " # string class and create single array.\n", " stopset = stopwords.words('english') + list(string.punctuation)\n", " # remove stop words and punctuations from string.\n", " # word_tokenize is used to tokenize the input corpus in word tokens.\n", " corpus = \" \".join([i for i in word_tokenize(corpus) if i not in stopset])\n", " # remove non-ascii characters\n", " corpus = unidecode(corpus)\n", " lemmatizer = WordNetLemmatizer()\n", " corpus = \" \".join([lemmatizer.lemmatize(w, get_wordnet_pos(w)) for w in nltk.word_tokenize(corpus)])\n", " return corpus\n", "\n", "def my_pre_process(x,silent=True):\n", "\n", " if not silent:\n", " print(\"\\n Printing First 5 rows \\n\")\n", " print(x[:5])\n", "\n", " x_2 = []\n", " all_tokens = []\n", "\n", " # nltk.download('stopwords')\n", "\n", " ps = PorterStemmer()\n", " stop_words = stopwords.words('english')\n", " tokenizer = RegexpTokenizer(r'\\w+')\n", "\n", " # Removing Punctuations\n", " for desc in x:\n", " # print(desc)\n", " all_tokens.append(tokenizer.tokenize(desc))\n", "\n", " # print(all_tokens)\n", "\n", " if not silent:\n", " print(\"\\n Pre-Processing Text \\n\")\n", " # PRE-PROCESSING TEXT\n", "\n", " # all_tokens contains list of list of descriptions like [[desc1_w1,desc1_w2],[desc2_w1,desc2_w2]...]\n", " for desc_tokens in all_tokens:\n", " # desc_tokens have first description sentence in list of tokens of words\n", " stemmed_desc_tokens = []\n", " for tok in desc_tokens:\n", " # tok is the first token word of from desc_tokens\n", " tok = tok.lower()\n", " # converted into lower case\n", " if tok not in stop_words:\n", " # if it is not in stop_words (stop_words is set having only .,\\! such punctuations)\n", " # so if it is not in stop_words that means its english word so all punctuations will be skipped\n", " stemmed_word = ps.stem(tok)\n", " # it is now done stemmed so root of that tok is stemmed_word\n", " # Now removing single character tokens which are totally useless for training\n", " if len(stemmed_word) > 1:\n", " # Now checking if the word which is stemmed is alphabetic only\n", " # then only add to the final list\n", " if stemmed_word.isalpha():\n", " stemmed_desc_tokens.append(stemmed_word)\n", "\n", " x_2.append(stemmed_desc_tokens)\n", "\n", " # print(x_2[:5])\n", "\n", " for index, desc_tokens in enumerate(x_2):\n", " desc = \" \".join(tok for tok in desc_tokens)\n", " x_2[index] = desc\n", "\n", " if not silent:\n", " print(\"\\n Printing First 5 rows AFTER PRE-PROCESSING : \\n\")\n", " print(x_2[:5])\n", "\n", " # Free memory\n", " x = x_2\n", " x_2 = None\n", "\n", " return x" ] }, { "cell_type": "code", "execution_count": 473, "metadata": {}, "outputs": [], "source": [ "df[\"bio\"] = df.apply(lambda row : my_pre_process([row[\"bio\"]])[0],axis=1)" ] }, { "cell_type": "code", "execution_count": 474, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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user_idbio
0fffe3100bottom line love life work hard love hard eart...
1fffe3200straightforward genuin fun love seriou profil ...
2fffe3300mmmmm yummi tacosss yoga love life part conten...
3fffe3400stealth geek special mix technic obsess enough...
4fffe3500whisper wind weav curl crash wave tide spit on...
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" ], "text/plain": [ " user_id bio\n", "0 fffe3100 bottom line love life work hard love hard eart...\n", "1 fffe3200 straightforward genuin fun love seriou profil ...\n", "2 fffe3300 mmmmm yummi tacosss yoga love life part conten...\n", "3 fffe3400 stealth geek special mix technic obsess enough...\n", "4 fffe3500 whisper wind weav curl crash wave tide spit on..." ] }, "execution_count": 474, "metadata": {}, "output_type": "execute_result" } ], "source": [ "index_values = list(df['user_id'])\n", "df[[\"user_id\",\"bio\"]].head()" ] }, { "cell_type": "code", "execution_count": 475, "metadata": {}, "outputs": [], "source": [ "from sklearn.feature_extraction.text import TfidfVectorizer\n", "tfidf_vectorizer = TfidfVectorizer(ngram_range=(1,2))\n", "tfidf_vectorizer.fit(df[\"bio\"])\n", "feature_vectors = tfidf_vectorizer.transform(df[\"bio\"])" ] }, { "cell_type": "code", "execution_count": 476, "metadata": {}, "outputs": [], "source": [ "df = pd.concat([df,pd.DataFrame(feature_vectors.toarray())],axis=1)\n", "df = df.drop([\"bio\"],axis=1)" ] }, { "cell_type": "code", "execution_count": 477, "metadata": {}, "outputs": [], "source": [ "df = df.drop([\"user_id\"],axis=1)\n", "\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "df = StandardScaler().fit_transform(df)" ] }, { "cell_type": "code", "execution_count": 478, "metadata": {}, "outputs": [], "source": [ "pca_components_per = 0.06\n", "from sklearn.decomposition import PCA\n", "pca = PCA(pca_components_per)\n", "pca = pca.fit(df)\n", "df = pca.transform(df)" ] }, { "cell_type": "code", "execution_count": 479, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2001, 17)" ] }, "execution_count": 479, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(df)\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 480, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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73.552126 \n", "fffe3200 -43.316582 -31.669941 -38.926270 \n", "fffe3300 60.023794 66.209502 54.270326 \n", "fffe3400 78.832997 87.468994 69.281964 \n", "fffe3500 87.031641 94.217932 85.229753 \n", "\n", " fffe3100390039003600 fffe3100390039003700 fffe3100390039003800 \\\n", "user_id \n", "fffe3100 80.069947 80.606996 78.808259 \n", "fffe3200 -39.843150 -34.750666 -19.067559 \n", "fffe3300 56.510472 61.928833 64.883286 \n", "fffe3400 87.996931 82.089754 84.067620 \n", "fffe3500 87.313497 95.588834 88.689893 \n", "\n", " fffe3100390039003900 fffe3200300030003000 fffe3200300030003100 \n", "user_id \n", "fffe3100 70.277476 50.520671 82.127116 \n", "fffe3200 -45.901212 -2.377834 -36.382437 \n", "fffe3300 56.311886 79.632286 64.547491 \n", "fffe3400 71.201980 63.368184 87.135369 \n", "fffe3500 86.689481 59.316524 94.778714 " ] }, "execution_count": 480, "metadata": {}, "output_type": "execute_result" } ], "source": [ "u_u_similarities = cosine_similarity(df)\n", "u_u_similarities = u_u_similarities*100\n", "\n", "df_to_submit = pd.DataFrame(data = u_u_similarities, \n", " index = index_values, \n", " columns =index_values)\n", "df_to_submit.index.name = \"user_id\"\n", "df_to_submit.head()" ] }, { "cell_type": "code", "execution_count": 481, "metadata": {}, "outputs": [], "source": [ "for x in index_values:\n", " df_to_submit.loc[x,x] = 0\n", "df_to_submit[df_to_submit < 0] = 0\n", "df_to_submit[df_to_submit > 100] = 100" ] }, { "cell_type": "code", "execution_count": 482, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fffe3100Edith Lopez27singlefgaysociallynever66.0medicine / healthoakland, californialikes dogs and likes catsnoenglish (fluently), spanish (poorly), sign lan...interestedathletic4.0nobottom line i love life! i work hard and i lov...sportsinstrumentssame statecaliforniaoakland
fffe3200Travis Young26singlemgaysociallynever68.0otherpleasant hill, californialikes dogsnoenglish (fluently), tagalog (okay), french (po...interestedfit3.0noi'm a straightforward, genuine, fun loving (i'...paintinginstrumentsanywherecaliforniapleasant hill
fffe3300Agnes Smith20seeing someonefbisexualsociallysometimes69.0otheroakland, californiahas dogs and likes catssometimesenglish (fluently), sign language (poorly), fr...interestedfit2.0nommmmm yummy tacosss. yoga is where it's at. i ...instrumentsdancingsame citycaliforniaoakland
fffe3400Salvador Klaver27singlembisexualsociallysometimes68.0computer / hardware / softwaredaly city, californialikes dogs and likes catsnoenglishnot interestedaverage3.0noi'm a stealth geek. that special mix of techni...sketchingactingsame citycaliforniadaly city
fffe3500Elana Sewell22singlefbisexualoftensometimes68.0otheroakland, californialikes dogs and likes catsyesenglishnot interestedaverage2.0yeswith the whisper of the wind i was weaved into...craftdesigningsame citycaliforniaoakland
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" ], "text/plain": [ " username age status sex orientation drinks \\\n", "user_id \n", "fffe3100 Edith Lopez 27 single f gay socially \n", "fffe3200 Travis Young 26 single m gay socially \n", "fffe3300 Agnes Smith 20 seeing someone f bisexual socially \n", "fffe3400 Salvador Klaver 27 single m bisexual socially \n", "fffe3500 Elana Sewell 22 single f bisexual often \n", "\n", " drugs height job \\\n", "user_id \n", "fffe3100 never 66.0 medicine / health \n", "fffe3200 never 68.0 other \n", "fffe3300 sometimes 69.0 other \n", "fffe3400 sometimes 68.0 computer / hardware / software \n", "fffe3500 sometimes 68.0 other \n", "\n", " location pets smokes \\\n", "user_id \n", "fffe3100 oakland, california likes dogs and likes cats no \n", "fffe3200 pleasant hill, california likes dogs no \n", "fffe3300 oakland, california has dogs and likes cats sometimes \n", "fffe3400 daly city, california likes dogs and likes cats no \n", "fffe3500 oakland, california likes dogs and likes cats yes \n", "\n", " language new_languages \\\n", "user_id \n", "fffe3100 english (fluently), spanish (poorly), sign lan... interested \n", "fffe3200 english (fluently), tagalog (okay), french (po... interested \n", "fffe3300 english (fluently), sign language (poorly), fr... interested \n", "fffe3400 english not interested \n", "fffe3500 english not interested \n", "\n", " body_profile education_level dropped_out \\\n", "user_id \n", "fffe3100 athletic 4.0 no \n", "fffe3200 fit 3.0 no \n", "fffe3300 fit 2.0 no \n", "fffe3400 average 3.0 no \n", "fffe3500 average 2.0 yes \n", "\n", " bio interests \\\n", "user_id \n", "fffe3100 bottom line i love life! i work hard and i lov... sports \n", "fffe3200 i'm a straightforward, genuine, fun loving (i'... painting \n", "fffe3300 mmmmm yummy tacosss. yoga is where it's at. i ... instruments \n", "fffe3400 i'm a stealth geek. that special mix of techni... sketching \n", "fffe3500 with the whisper of the wind i was weaved into... craft \n", "\n", " other_interests location_preference location_state location_city \n", "user_id \n", "fffe3100 instruments same state california oakland \n", "fffe3200 instruments anywhere california pleasant hill \n", "fffe3300 dancing same city california oakland \n", "fffe3400 acting same city california daly city \n", "fffe3500 designing same city california oakland " ] }, "execution_count": 482, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(filepath_or_buffer=\"./dataset/data.csv\")\n", "\n", "df[\"location_state\"] = None\n", "df[\"location_city\"] = None\n", "\n", "for ind in df.index:\n", " \n", " location = df[\"location\"][ind]\n", " x = location.split(\",\")\n", " x[0] = x[0].strip()\n", " x[1] = x[1].strip()\n", " df.loc[ind,\"location_state\"] = x[1]\n", " df.loc[ind,\"location_city\"] = x[0]\n", "\n", "df.set_index(\"user_id\", inplace=True)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 483, "metadata": {}, "outputs": [], "source": [ "for ind in df_to_submit.index:\n", " my_info = df.loc[ind]\n", " my_gender = my_info[\"sex\"]\n", " my_orientation = my_info[\"orientation\"]\n", " if my_orientation == \"straight\":\n", " tmp = df.loc[(df[\"orientation\"] == \"straight\") & (df[\"sex\"] == my_gender)]\n", " tmp_index = list(tmp.index)\n", " df_to_submit.loc[ind,tmp_index] = 0\n", " elif my_orientation == \"gay\":\n", " tmp = df.loc[(df[\"orientation\"] == \"gay\") & (df[\"sex\"] != my_gender)]\n", " tmp_index = list(tmp.index)\n", " df_to_submit.loc[ind,tmp_index] = 0\n", " \n", " my_location_preference = my_info[\"location_preference\"]\n", " my_state = my_info[\"location_state\"]\n", " my_city = my_info[\"location_city\"]\n", " if my_location_preference == \"same city\":\n", " tmp = df.loc[df[\"location_city\"] != my_city]\n", " tmp_index = list(tmp.index)\n", " df_to_submit.loc[ind,tmp_index] = 0\n", " elif my_location_preference == \"same state\":\n", " tmp = df.loc[df[\"location_state\"] != my_state]\n", " tmp_index = list(tmp.index)\n", " df_to_submit.loc[ind,tmp_index] = 0 " ] }, { "cell_type": "code", "execution_count": 484, "metadata": {}, "outputs": [], "source": [ "df_to_submit.to_csv(path_or_buf=\"./df_to_submit_location_\"+str(pca_components_per*100)+\".csv\",float_format='%.1f')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 4 }